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Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed. To resolve them, we propose Kn